The invention relates to a method to detect an impurity in a sample which can be applied to chromatographic peak analysis through manipulation of spectrophotometric readings, and more particularly to readings detected from a photodiode-array (PDA) detector.
Methods for the detection and identification of multiple components in a sample using the principles of liquid chromatography (LC) and spectrophotometry are well known to those skilled in the art. The identification of specific unknown components in the sample solution first requires a physical separation of any mixed components using liquid chromatography. The sample is eluted or washed through an LC column using a mobile phase of solvents known to be appropriate for the particular separation sought and system used. Separation is achieved with the LC column based upon a particular component""s adsorptive affinity to the media packed in the LC column. The eluted sample is then analyzed using UV-VIS spectrophotometry. The particular chemical structure of the analyte when subjected to radiation in the ultraviolet-visible (UV-VIS) range of 200-800 nm will have a characteristic spectral response due to the absorption of radiation when detected with a photodiode-array (PDA) detector. The (PDA) detector measures the transmitted energy and converts it into absorption units (AU) as a function of elution time (tR) and wavelength (xcex).
Detection of impurities poses a problem when the particular chemical structure of the impurities is such that they become co-eluted with the analyte of interest. When this occurs the impurity is often difficult to detect because the chromatomographic separation does not sufficiently resolve the different components and their spectral peaks are overlapped. In the case of a single wavelength UV/visible detector one might see a xe2x80x9cshoulderxe2x80x9d, xe2x80x9cvalleyxe2x80x9d or xe2x80x9cexcessive tailingxe2x80x9d in the chromatographic peak to suspect the presence of an impurity. Moreover the absence of these features on the chromatographic peak does not assure that the component represented by the peak is pure. The impurity could simply be cloaked or xe2x80x9cnot seenxe2x80x9d within the larger peak that is detected by the spectrophotometer because the chromatographic resolution was too low or the purity concentration was too low.
Several approaches have been taken to detect the presence of an impurity co-eluting with the analyte peak. M.V. Gorenstein, et al., xe2x80x9cDetecting Co-Eluted Impurities by Spectral Comparisonxe2x80x9d, LC-GC. 12, No. 10, 768-772 (1994), incorporated herein by reference, discloses a two-step spectral-contrast technique which first generates vector angles which measure the spectral heterogeneity of a given peak and the shape difference between two spectra. Then a quantification for non-ideal effects such as noise contribution is made and assigned as a threshold vector angle. These two angles are compared along an analyte""s chromatogram. The procedure correlates the difference between the two angles with detection of an impurity in a chromatographic peak. However, in order to accomplish this there must be a baseline correction for each spectrum within the peak which involves interpolation of peak xe2x80x9cstartxe2x80x9d and xe2x80x9cendxe2x80x9d spectra to obtain a series of underlying baseline spectra. In addition, the vector angles used to determine whether an impurity exists do not directly calculate or quantify the spectral error in the measured absorbence units caused by the impurity. Also, such a technique requires separate calibration runs to establish a threshold, and will have difficulties with multiple impurities.
Y. Hu, et al., xe2x80x9cAssessment of Chromatographic Peak Purity By Means Of Artificial Neural Networksxe2x80x9d J. Chromatogr. 734, 259-270 (1996), incorporated herein by reference, discloses a three-layer neural network for detecting the purity of a chromatographic peak. The method relies on applying a neural network to the difference between the spectra within the chromatographic peak to be analyzed. Applications of neural networks are known to those skilled in the art, and as disclosed by Hu in this application, involve training sets of data for the front half of each peak and testing these data sets for recognition responses against the back half of each peak using artificial neural network techniques. This approach strongly depends on the retention time and number of impurities with respect to the main components(s) due to the somewhat arbitrary partitioning between a front and back part of a chromatogram. Another disadvantage to this neural network approach is that approximately half of the data associated with a component""s chromatogram and spectra are used which can compromise the detection sensitivity for analytes where the chromatographic retentions and spectra of the main component substance and impurity are strongly overlapped. Moreover, this approach is incapable of performing a quantitative analysis concerning impurity level.
What is desired, therefore, is a method to detect and quantify an impurity from an LC peak using UV-VIS spectrophotometry with a PDA detector where a measure of peak impurity can be reported in natural units across a whole chromatographic peak and where noise filtering is automatically built-in.
Accordingly, an object of the invention is to provide a method applying an algorithm for LC impurity detection where a peak purity index can be reported across the entire chromatographic peak.
Another object of the invention is to provide a method applying an algorithm for LC impurity detection that is not sensitive to detecting a peak start and a peak end for a chromatographic peak.
A further object of the invention is to provide a method applying an algorithm for LC impurity detection with signal averaging or noise-filtering automatically built-in.
Yet another object of the invention is to provide a method applying an algorithm for LC impurity detection which can detect not only an impurity co-existing with one major component but also an impurity in the presence of n major components.
Another object of the invention is to provide a method applying an algorithm for LC impurity detection where a peak index is reported in original spectral absorbence units which can be directly interpreted as the amount of spectral error caused by the impurity.
A further object of the invention is to provide a method applying an algorithm for LC impurity detection that requires no calibration or standard runs to detect the impurity.
Still another object of the invention is to provide a method applying an algorithm for LC impurity detection based on robust matrix algebra representing the entire spectral space corresponding to the chromatographic peak.
To overcome some of the disadvantages of the prior art and achieve some of the objects and advantages listed above, the present invention comprises: a method for detecting an impurity in a sample, and in a further aspect, an apparatus for detecting an impurity in a sample.
A method according to the invention for detecting an impurity in a sample having at least one analyte comprises the steps of: selecting a value representing an anticipated number of components in the sample; generating a matrix representing characteristic measurements for the sample, the characteristic measurements having at least two variables in each dimension; repeatedly selecting a subset within the matrix for analysis of the relation between the analyte and impurity; and calculating an index from the subset to assess purity of the sample.
The invention in one of its aspects also provides an apparatus for detecting an impurity in a sample having at least one component comprising: a device for obtaining characteristic measurements for the sample; a computer linked to the device; software executing on the computer for selecting a value representing an anticipated number of components in the sample; software executing on the computer for generating a matrix representing the characteristic measurements for the sample, the characteristic measurements having at least two variables in each dimension and; software executing on the computer for repeatedly selecting a subset within the matrix for analysis of the relation between the component and the impurity; and software executing on the computer for calculating an index from the subset to assess purity of the sample.